Introduction to Python 2400-MIKRO-AIBF-IP
The course is delivered asynchronously through an online learning platform and is intended to prepare participants to use Python during the SPINAKER Summer School. It is designed both for beginners and for participants who already have some experience with Python but need to refresh or systematise their knowledge. The course begins with a diagnostic test that enables participants to assess their current skill level and determine the amount of preparatory work they should complete before the summer school begins.
Starting with the configuration of the Python working environment, the course progresses to basic data manipulation and visualisation, preparing participants for further, more advanced applications of Python in data analysis.
The course covers eight topics:
● Introduction to Python and development environments (VS Code / Jupyter)
● Understanding Python data types and structures
● Fundamentals of data analysis and statistical modelling in Python
● Data visualisation using Matplotlib and Seaborn
● Loops and functions in Python
● Data manipulation using Pandas
● Working with Jupyter Notebook and documentation
● Additional module: how to write clean, readable and reproducible Python code
Each module includes a recorded lecture, a presentation, a Python script (Jupyter Notebook) containing examples and practical exercises, as well as assignments for independent completion. At the end of each module, participants complete a short graded quiz. The quizzes may be attempted multiple times, and the highest score is recorded for assessment. After completing the teaching modules, participants undertake a final data analysis case study, allowing them to assess how independently and confidently they can use Python to complete an end-to-end analytical task.
The project ‘Application of Artificial Intelligence in Business and Finance’ (No. BPI/SPI/2024/1/00078) is implemented by the University of Warsaw as part of the Spinaker programme organised by the National Agency for Academic Exchange (within the project ‘Wsparcie tworzenia i realizacji międzynarodowych programów kształcenia’, funded by the European Funds for Social Development 2021–2027).
Course coordinators
Micro-credential certificate
Learning outcomes
Upon completion of the course, the participant:
in terms of knowledge:
knows and understands the basic Python working environment and its tools (Python interpreter, pip, VS Code, Jupyter Notebook); knows and understands basic Python data types and structures (int, float, str, bool, list); knows and understands the fundamentals of data analysis and statistical modelling, data visualisation, loops, functions and data manipulation in Pandas; knows and understands the basic principles of writing clean, readable and reproducible code.
in terms of skills:
is able to install and configure a Python working environment; is able to write and run simple Python scripts (.py files) and Jupyter notebooks; is able to define and use variables, basic data types and lists; is able to write simple loops and functions; is able to perform basic data manipulation in Pandas and create simple visualisations in Matplotlib and Seaborn; is able to use the official documentation and built-in help to solve simple problems independently.
in terms of social competences:
is prepared to approach programming problems systematically and debug code using error messages and documentation; is prepared to continue learning independently using official Python resources (documentation, PyPI and community resources).
Assessment criteria
Written assessment (post-test)
Bibliography
1. Official Python documentation — https://docs.python.org
2. Python Package Index (PyPI) — https://pypi.org
3. W3Schools Python Tutorial — https://www.w3schools.com/python/